Recognition and prediction of lane constraints and construction areas in navigation

ABSTRACT

Systems and methods use cameras to provide autonomous and/or driver-assist navigation features. In some implementations, techniques for predicting the location of first roadway lane constraints are provided. The system may receive multiple images of a roadway in a vicinity of a vehicle, recognize a first roadway lane constraint, and, when lane prediction conditions are determined to be satisfied, predict a location of a second roadway lane constraint. In some implementations, techniques for detecting and responding to construction zones are provided. The system may receive multiple images of a roadway in a vicinity of a vehicle, recognize indicators of a construction zone in the images, determine that the vehicle is proximate to a construction zone, and output a signal indicating that the vehicle is proximate to a construction zone.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/038,426, the entirety of which is incorporated hereinby reference.

FIELD OF THE DISCLOSURE

This disclosure relates generally to autonomous driving and/or driverassist technology and, more specifically, to systems and methods thatuse cameras to provide autonomous driving and/or driver assisttechnology features.

BACKGROUND

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Primarily, an autonomous vehicle may be able to identify its environmentand navigate without input from a human operator. Autonomous vehiclesmay also take into account a variety of factors and make appropriatedecisions based on those factors to safely and accurately reach anintended destination. For example, various objects—such as othervehicles and pedestrians—are encountered when a vehicle typicallytravels a roadway. Autonomous driving systems may recognize theseobjects in a vehicle's environment and take appropriate and timelyaction to avoid collisions. Additionally, autonomous driving systems mayidentify other indicators—such as traffic signals, traffic signs, andlane markings—that regulate vehicle movement (e.g., when the vehiclemust stop and may go, a speed at which the vehicle must not exceed,where the vehicle must be positioned on the roadway, etc.). Autonomousdriving systems may need to determine when a vehicle should changelanes, turn at intersections, change roadways, etc. As is evident fromthese examples, many factors may need to be addressed in order toprovide an autonomous vehicle that is capable of navigating safely andaccurately.

SUMMARY

Current autonomous or driver-assist (e.g., partially autonomous) vehiclesystems may be unable to adequately identify a lane when lane lines(e.g., the marked lines that divide lanes) or other markings do notclearly indicate the locations of lane constraints on a roadway. Forexample, current systems may not function properly when a laneconstraint cannot be identified from lines or markings detected inreceived images of a roadway; or, current systems may inaccuratelyidentify a lane constraint where a lane constraint does not in realityexist. Current autonomous or driver-assist vehicle systems may also beunable to adequately identify or respond to construction zones onroadways, where modified vehicle control may be required, and whereresponding to certain roadway elements in a different manner may beadvantageous. The inventors have recognized these and other problemswhich would be apparent from the present disclosure, which are addressedby the techniques disclosed herein.

Some embodiments consistent with the present disclosure provide systemsand methods for autonomous or driver-assist vehicle navigation. Somedisclosed embodiments may use cameras to provide autonomous vehiclenavigation features. For example, consistent with some disclosedembodiments, the disclosed systems may include one, two, or more camerasthat monitor the environment of a vehicle and cause a navigationalresponse based on an analysis of images captured by one or more of thecameras.

In accordance with some embodiments, a computer system comprises amemory that stores instructions, and a processor that executes theinstructions to cause the system to: receive, from a camera, multipleimages of a roadway in a vicinity of a moving vehicle, recognize, basedon the multiple images, a first roadway lane constraint, determine,based on the multiple images, that one or more defined lane predictionconditions are satisfied, in accordance with a determination that one ormore defined lane prediction conditions are satisfied, predict alocation of a second roadway lane constraint, and enable the movingvehicle to avoid the first roadway lane constraint and the secondroadway lane constraint.

In accordance with some embodiments, a computer system comprises amemory that stores instructions and a processor that executes theinstructions to cause the system to: receive, from a camera, multipleimages of a roadway in a vicinity of a moving vehicle, recognize, in themultiple images, one or more indicators of a construction zone,determine, based on the one or more indicators, that the vehicle isproximate to a construction zone, and, in accordance with adetermination that the vehicle is proximate to a construction zone,output a signal indicating that the vehicle is proximate to aconstruction zone.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIGS. 8A-8C are depictions of a vehicle traveling along a roadway inaccordance with some embodiments.

FIGS. 9A-9C are depictions of a vehicle traveling along a roadway inaccordance with some embodiments.

FIG. 10A is a depiction of a perspective view from a camera associatedwith a system in accordance with some embodiments.

FIG. 10B is a depiction of a vehicle traveling along a roadway inaccordance with some embodiments.

FIG. 11 is a diagrammatic representation of the memory of an exemplarysystem consistent with some disclosed embodiments.

FIG. 12 is a flowchart of an exemplary process in accordance with someembodiments.

FIG. 13 is a depiction of a vehicle traveling along a roadway inaccordance with some embodiments.

FIG. 14 is a diagrammatic representation of the memory of an exemplarysystem consistent with some disclosed embodiments.

FIG. 15 is a flowchart of an exemplary process in accordance with someembodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples.

Disclosed embodiments provide systems and methods that use cameras toprovide autonomous navigation, vehicle control, and/or driver assisttechnology features. Driver assist technology, as opposed to fullyautonomous driving, refers to any suitable technology to assist driversin the navigation and control of their vehicles, such as LKA (lanekeeping assist), LDW (lane departure warning), acceleration of thevehicle, deceleration of the vehicle, steering of the vehicle,controlling vehicle braking, preparing the vehicle for an unavoidablecollision, and/or any other suitable manner of controlling any aspect ofthe operation of the vehicle or assisting the driver in controlling anysuch aspect. In various embodiments, the system may include one, two ormore cameras that monitor the environment of a vehicle. In someembodiments, the system may provide techniques for predicting thelocation of roadway lane constraints and/or techniques for detecting andresponding to construction zones.

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, and a user interface 170. Processingunit 110 may include one or more processing devices. In someembodiments, processing unit 110 may include an application processor180, an image processor 190, or any other suitable processing device.Similarly, image acquisition unit 120 may include any number of imageacquisition devices and components depending on the requirements of aparticular application. In some embodiments, image acquisition unit 120may include one or more image capture devices (e.g., cameras), such asimage capture device 122, image capture device 124, and image capturedevice 126. System 100 may also include a data interface 128communicatively connecting processing unit 110 to image acquisitiondevice 120. For example, data interface 128 may include any wired and/orwireless link or links for transmitting image data acquired by imageaccusation device 120 to processing unit 110.

Both application processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplication processor 180 and image processor 190 may include one ormore microprocessors, preprocessors (such as image preprocessors),graphics processors, central processing units (CPUs), support circuits,digital signal processors, integrated circuits, memory, or any othertypes of devices suitable for running applications and for imageprocessing and analysis. In some embodiments, application processor 180and/or image processor 190 may include any type of single or multi-coreprocessor, mobile device microcontroller, central processing unit, etc.Various processing devices may be used, including, for example,processors available from manufacturers such as Intel®, AMD®, etc. andmay include various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, application processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture has two floating point, hyper-thread 32-bit RISC CPUs(MIPS32® 34K® cores), five Vision Computing Engines (VCE), three VectorMicrocode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of application processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices for image processing and analysis. The imagepreprocessor may include a video processor for capturing, digitizing andprocessing the imagery from the image sensors. The CPU may comprise anynumber of microcontrollers or microprocessors. The support circuits maybe any number of circuits generally well known in the art, includingcache, power supply, clock and input-output circuits. The memory maystore software that, when executed by the processor, controls theoperation of the system. The memory may include databases and imageprocessing software. The memory may comprise any number of random accessmemories, read only memories, flash memories, disk drives, opticalstorage, tape storage, removable storage and other types of storage. Inone instance, the memory may be separate from the processing unit 110.In another instance, the memory may be integrated into the processingunit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., application processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware. The memory units may include random access memory, read onlymemory, flash memory, disk drives, optical storage, tape storage,removable storage and/or any other types of storage. In someembodiments, memory units 140, 150 may be separate from the applicationprocessor 180 and/or image processor 190. In other embodiments, thesememory units may be integrated into application processor 180 and/orimage processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplication processor 180 and/or image processor 190.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, application processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.).

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 28, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, motorcycles, bicycles,self-balancing transport devices and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or different than (e.g., narrower than), a FOV(such as FOV 202) associated with image capture device 122. For example,image capture devices 124 and 126 may have FOVs of 40 degrees, 30degrees, 26 degrees, 23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by dx, as shown in FIGS. 2C and 2D. In some embodiments, fore oraft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

In some embodiments, a system may define the “vicinity” of the vehicleaccording to predefined constraints or dynamically determinedconstraints. The vicinity of the vehicle may, for example, be consideredany area within a certain distance of the vehicle in any one or moredirections, such as 1 meter, 5 meters, 10 meters, 25 meters, 50 meters,100 meters, 250 meters, 500 meters, or 1000 meters. In some embodiments,the area defined as the vicinity of the vehicle may be irregularlyshaped, such that it extends for different distances in differentdirections; the vicinity in the forward direction as defined by thevehicle's travel may, for example, extend for several hundred meters,while the vicinity in the backward or sideways directions may onlyextend, for example, for several dozen meters or less. The definedvicinity of the vehicle may, in some embodiments, be varied dynamicallyaccording to the information captured by the one or more image capturedevices, such that the vicinity may vary according to, for example, whatparts of a roadway are visible in the FOV of the image capture devices.The vicinity of the vehicle may, in some embodiments, be dynamicallyvaried according to, for example, any perceptible condition or state ofthe roadway or vehicle, including vehicle speed, posted speed limit,roadway conditions, roadway congestion, roadway size, visibilitydistance, environment brightness, time of day, presence of othervehicles, presence of pedestrians, or any other suitable factor.

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 240indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that it aligns against a vehicle windshield having amatching slope. In some embodiments, each of image capture devices 122,124, and 126 may be positioned behind glare shield 380, as depicted, forexample, in FIG. 3D. The disclosed embodiments are not limited to anyparticular configuration of image capture devices 122, 124, and 126,camera mount 370, and glare shield 380. FIG. 3C is an illustration ofcamera mount 370 shown in FIG. 3B from a front perspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and, in response to thisanalysis, navigate and/or otherwise control and/or operate vehicle 200.Navigation, control, and/or operation of vehicle 200 may includeenabling and/or disabling (directly or via intermediary controllers,such as the controllers mentioned above) various features, components,devices, modes, systems, and/or subsystems associated with vehicle 200.Navigation, control, and/or operation may alternately or additionallyinclude interaction with a user, driver, passenger, passerby, and/orother vehicle or user, which may be located inside or outside vehicle200, for example by providing visual, audio, haptic, and/or othersensory alerts and/or indications.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings, indications, recommendations, alerts, orinstructions to a driver, passenger, user, or other person inside oroutside of the vehicle (or to other vehicles) based on the analysis ofthe collected data.

Further, consistent with disclosed embodiments, the functionalityprovided by system 100 may cause vehicle 200 to take different actionsto navigate vehicle 200 within a lane and/or relative to other vehiclesand/or objects. For example, system 100 may adjust the positioning ofvehicle 200 relative to a lane within which vehicle 200 is travelingand/or relative to objects positioned near vehicle 200, select aparticular lane for vehicle 200 to use while traveling, and take actionin response to an encroaching vehicle, such as a vehicle attempting tomove into the lane within which vehicle 200 is traveling. Additionally,system 100 may control the speed of vehicle 200 in different scenarios,such as when vehicle 200 is making a turn. System 100 may cause vehicle200 to mimic the actions of a leading vehicle or monitor a targetvehicle and navigate vehicle 200 so that it passes the target vehicle.Additional details regarding the various embodiments that are providedby system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122-126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122-126 may be positioned behind rearviewmirror 310 and positioned substantially side-by-side (e.g., 6 cm apart).Further, in some embodiments, as discussed above, one or more of imagecapture devices 122-126 may be mounted behind glare shield 380 that isflush with the windshield of vehicle 200. Such shielding may act tominimize the impact of any reflections from inside the car on imagecapture devices 122-126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122-126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data (e.g., a depth map) or with 3Dinformation calculated based on information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images or with thedepth information obtained by stereo processing.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may execute additional processing instructions toanalyze images to identify objects moving in the image, such as vehicleschanging lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, application processor 180 and/or image processor190 may execute the instructions stored in any of modules 402-408included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto application processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar) to perform the monocular image analysis. As described inconnection with FIGS. 5A-5D below, monocular image analysis module 402may include instructions for detecting a set of features within the setof images, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, hazardous objects, and any otherfeature associated with an environment of a vehicle. In someembodiments, an environment of a vehicle may include, for any givenframe, the area or space covered by any one or by any combination of theimaging devices (the FOV of the imaging devices). Based on the analysis,system 100 (e.g., via processing unit 110) may cause one or morenavigational responses in vehicle 200, such as a turn, a lane shift, achange in acceleration, and the like, as discussed below in connectionwith navigational response module 408. Furthermore, based on theanalysis, system 100 (e.g., via processing unit 110) may cause one ormore navigational and/or vehicle control responses, including but notlimited to the various manners of controlling vehicle 200 and anyassociated systems and/or subsystems discussed above. In someembodiments, any or all of the responses or actions mentioned above maybe referred to, for simplicity, as a navigational action or anavigational response.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad or of certain areas of the road (e.g., within a region of interestdefined around image data which is suspected to show a road hazard).Processing unit 110 may then use the 3D-map to detect road hazards. Forexample, segmentation techniques and classification techniques can beused to detect the road surface, as well as hazards existing above theroad surface. For example, as explained in additional detail below, asystem may detect features of a road surface such as painted lane linesor lane markings, and may additionally detect features of a roadwayexisting above the road surface, such as cones/barrels/poles/barriers ina construction zone, guard rails, other vehicles, road signs, and thelike.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example, size,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550-556, processing unit 110 may identify road marksappearing within the set of captured images and derive lane geometryinformation. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560-564, processing unit 110 mayidentify traffic lights appearing within the set of captured images andanalyze junction geometry information. Based on the identification andthe analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and, in some embodiments, the distance di betweentwo points in the set of points may fall in the range of 1 to 5 meters,or in further examples, a timing measure can be used to provide the setof points, whereby the points in the set of points are 0.5 second to 5seconds apart from one another (e.g., 1.5 seconds apart). In oneembodiment, processing unit 110 may construct the initial vehicle pathusing two polynomials, such as left and right road polynomials.Processing unit 110 may calculate the geometric midpoint between the twopolynomials and offset each point included in the resultant vehicle pathby a predetermined offset (e.g., a smart lane offset), if any (an offsetof zero may correspond to travel in the middle of a lane). The offsetmay be in a direction perpendicular to a segment between any two pointsin the vehicle path. In another embodiment, processing unit 110 may useone polynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x₁, z₁)) based on the updated vehicle pathconstructed at step 572. Processing unit 110 may extract the look-aheadpoint from the cumulative distance vector S, and the look-ahead pointmay be associated with a look-ahead distance and look-ahead time. Thelook-ahead distance, which may have a lower bound ranging from 10 to 20meters, may be calculated as the product of the speed of vehicle 200 andthe look-ahead time. For example, as the speed of vehicle 200 decreases,the look-ahead distance may also decrease (e.g., until it reaches thelower bound). The look-ahead time, which may range from 0.5 to 1.5seconds, may be inversely proportional to the gain of one or morecontrol loops associated with causing a navigational response in vehicle200, such as the heading error tracking control loop. For example, thegain of the heading error tracking control loop may depend on thebandwidth of a yaw rate loop, a steering actuator loop, car lateraldynamics, and the like. Thus, the higher the gain of the heading errortracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x₁/z₁). Processingunit 110 may determine the yaw rate command as the product of theheading error and a high-level control gain. The high-level control gainmay be equal to: (2/look-ahead time), if the look-ahead distance is notat the lower bound. Otherwise, the high-level control gain may be equalto: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Lane Constraint Prediction

System 100 may provide vehicle 200 with lane constraint predictiontechniques, such that the system predicts the location of one or morelane constraints.

In some embodiments, lane constraint prediction may be understood as oneor more techniques for predicting the location of a lane constraint. Alane constraint, in some embodiments, may be defined as the boundary atthe edge of a lane, roadway, parking space, or other path or space inwhich a vehicle should be caused to travel. In some situations, a laneconstraint may be marked on a roadway, such as by painted lines, aphysical barrier, Botts' dots, or one or more additional indicators. Insome embodiments, a lane constraint may be defined by the edge of aroadway, a curb, a guard-rail, or the like. In some embodiments, a laneconstraint may be defined by one or more other vehicles, such as a rowof parallel-parked vehicles adjacent to the lane, or a row of parkedvehicles in a parking lot. In some embodiments, a lane constraint may beunmarked, such as on an unmarked roadway or in a parking lot withoutlane lines.

In some embodiments, a self-driving or driver-assist system may useimage capture devices and image analysis to determine the location ofone or more lane constraints based on objects detected in the images,such as lane markings, edges of roadways, and/or other vehicles. In mostdriving situations, the system may be able to detect at least two laneconstraints—one on each side of the lane in which the vehicle istraveling—based on the visible characteristics (e.g., lane lines, etc.)of the captured images.

In certain situations, however, including those in which only one laneconstraint on one side of a lane or on one side of vehicle 200 can besufficiently detected based on visible characteristics in the capturedimages (e.g., lane lines, etc.), system 100 may predict the position ofa lane constraint on the other side of the lane or on the other side ofthe vehicle, allowing the vehicle to continue to travel within a lanedefined by the detected constraint and the predicted constraint oppositeit.

In some embodiments, lane constraint prediction may be performed bysystem 100 subject to one or more conditions being satisfied. Forexample, in some embodiments, system 100 predicts the location of a laneconstraint when a previously-detected lane constraint ceases to bedetected as vehicle 200 progresses along a roadway. Thus, in someembodiments if a lane marking or other detected lane constraint suddenlyends or becomes undetectable, then lane constraint prediction may beperformed. In other examples, system 100 predicts the location of a laneconstraint when the system determines that guard-rail shadow conditionsare present, such that a guard rail is present in the vicinity of theroadway or a guardrail is casting a shadow onto the roadway.

In some embodiments, system 100 may only perform lane constraintprediction when system 100 has access to valid and sufficient historicalinformation about the width of a lane in which vehicle 200 is traveling;in situations in which a lane constraint on one side of vehicle 200 isnewly detected as vehicle 200 progresses along a roadway, system 100 mayin some embodiments refrain from performing lane constraint prediction.

In some embodiments, lane constraint prediction may be performed for amaximum travel distance, such that, if vehicle 200 travels greater thana predefined maximum travel distance while system 100 is continuouslyperforming lane constraint prediction, then lane constraint predictionmay then be ceased. In some embodiments, the predefined maximum traveldistance may be set or modified in accordance with one or more variousfactors detected by system 100. In some embodiments, the predefinedmaximum travel distance may be turned off (e.g., it may be functionallyset to “infinite”) in accordance with one or more various factorsdetected by system 100.

In some embodiments, lane constraint prediction may be ceased inaccordance with various cancellation conditions detected by system 100.For example, in some embodiments, if a driver of vehicle 200 changeslanes, then lane constraint prediction may be ceased. In someembodiments, if vehicle 200 is traversing a curve in the roadway thatexceeds a predefined curve sharpness threshold, then lane constraintprediction may be ceased.

FIGS. 8A-8C illustrate an embodiment in which vehicle 200 travels on aroadway 800. In some embodiments, vehicle 200 may have one or morefields of vision provided by one or more image capture devices orcameras, such as image capture devices 122, 124, and/or 126. In thedepicted example, vehicle 200 may have a field of vision facingfrontward from the vehicle, as depicted by the angled lines forming anupward-opening triangle in front of vehicle 200; in some embodiments,vehicle 200 may have one or more fields of vision facing in multipledifferent directions from vehicle 200. In the depicted example, thedisclosed systems and methods for lane constraint prediction (e.g.,predicting the location of a lane constraint when one is not detected)and operating vehicle 200 within a lane defined by a predicted laneconstraint may be used. As shown, roadway 800 may have lane 802 in whichvehicle 200 is traveling. Lane 802 may be defined on a first side byfirst lane constraint 804 and on a second side, opposite the first side,by lane constraint 806. These lane constraints 804 and 806 may bedetected by system 100, as indicated by the bolded lines showing laneconstraints 804 and 806. Vehicle 200 may have a first vehicle side 808on the same side of vehicle 200 as first lane constraint 804, and asecond vehicle side 810 on the same side of vehicle 200 as second laneconstraint 806.

Processing unit 110 may be configured to determine first lane constraint804 and second lane constraint 806 based on a plurality of imagesacquired by image capture device 122-126 that processing unit 110 mayreceive via data interface 128. According to some embodiments, firstlane constraint 804 and/or second lane constraint 806 may be identifiedby visible lane boundaries, such as dashed or solid lines marked on aroad surface, such as lane lines 803 a (solid line) and 803 b (dashedline). Additionally or alternatively, first lane constraint 804 and/orsecond lane constraint 806 may be identified by an edge of a roadsurface or a barrier. Additionally or alternatively, first laneconstraint 804 and/or second lane constraint 806 may be identified bymarkers (e.g., Botts' dots). According to some embodiments, processingunit 110 may determine first lane constraint 804 and/or second laneconstraint 806 by identifying a midpoint of a road surface width.According to some embodiments, if processing unit 110 identifies onlyfirst lane constraint 804, processing unit 110 may estimate second laneconstraint 806, such as based on an estimated lane width or road width.Processing unit 110 may predict lane constraints in this manner when,for example, lines designating road lanes are not painted or otherwiselabeled.

In FIGS. 8A-8C, lane constraints 804 and 806 are marked on roadway 800by solid and dashed lines 803 a and 803 b, respectively. In a portion ofroadway 800 toward the center of FIGS. 8A-8C, dashed line 803 bcorresponding to lane constraint 806 is not painted on roadway 800. InFIGS. 8A-8C, the detection of lane constraints 804 and 806 by system 100is depicted by the bolded lines that overlay the respective lane linesin front of vehicle 200. As depicted by the bolded lines in FIG. 8A,system 100 detects lane constraint 804 as a solid-line lane constrainton the left side of vehicle 200 for a certain visible distance in frontof vehicle 200. Similarly, system 100 detects lane constraint 806 as adashed-line lane constraint on the right side of vehicle 200 for acertain visible distance in front of vehicle 200. In accordance with thedetection of lane constraints 804 and 806, system 100 causes vehicle 200to travel inside lane 802 as defined by lane constraints 804 and 806.

FIG. 8B depicts vehicle 200 continuing to progress along roadway 800 inlane 802 following the point in time depicted in FIG. 8A. As vehicle 200reaches the portion of roadway 800 at which dashed line 803 b is notpainted on roadway 800, lane constraint prediction may be performed bysystem 100. As shown, system 100 predicts the location of laneconstraint 806 as being located a distance 812 from opposite laneconstraint 804. This distance 812 may be determined, in someembodiments, based on the historical information detected and stored bysystem 100 about the previous widths of lane 802 and/or the averagehistorical width of lanes in general. As shown, system 100 predicts thatlane constraint 806 continues to be a dashed lane constraint, based onthe fact that lane constraint 806 was marked by dashed lane line 803 bat the last time that lane constraint 806 was detected by system 100before lane constraint prediction was activated. In accordance with thecontinued detection of lane constraint 804 and the prediction of laneconstraint 806, system 100 causes vehicle 200 to continue to travelinside lane 802 as defined by lane constraints 804 and 806. In someembodiments, when lane constraint prediction is activated, and possiblyunder certain circumstances (e.g., there are other vehicles nearby), acertain default type of lane constraint is used to control the motion ofthe vehicle which is not necessarily the same as the last laneconstraint that was detected previous to the activation of the laneconstraint prediction. For example, in some embodiments, a system maydefault to predicting solid-line lane constraints rather thandashed-line lane constraints.

FIG. 8C depicts vehicle 200 continuing to progress along roadway 800 inlane 802 following the point in time depicted in FIG. 8B. As vehicle 200reaches the portion of roadway 800 at which dashed line 803 b is onceagain painted on roadway 800, lane constraint prediction may cease to beperformed by system 100, as lane constraint 806 is once again detected(rather than predicted) as a dashed lane constraint by system 100. Asdepicted in FIG. 8C, vehicle 200 has traveled distance 814 whileperforming lane constraint prediction. In some embodiments, laneconstraint prediction may only be performed continuously orsubstantially continuously for a predefined maximum distance (and/orpredefined maximum time). In the depicted example of FIG. 8C, distance814 is less than the predefined maximum distance for performing laneconstraint prediction, such that lane constraint prediction was able tobe performed continuously as vehicle 200 traversed the distance betweenits position in FIG. 8B and its position in FIG. 8C. In someembodiments, the predefined maximum distance for performing laneconstraint prediction may be set to a default of 18 meters. In someembodiments, the predefined maximum distance may be varied by vehiclemanufacturers, drivers, and/or any other party.

FIGS. 9A-9C illustrate an embodiment in which vehicle 200 travels on aroadway 900. In the depicted example, the disclosed systems and methodsfor lane constraint prediction (e.g., predicting the location of a laneconstraint when one is not detected) and operating vehicle 200 within alane defined by a predicted lane constraint may be used. The exampledepicted in FIGS. 9A-9C differs from the example depicted in FIGS. 8A-8Cat least in that, on roadway 900, two lanes are merging into one lane.As explained in greater detail below, in some embodiments, laneconstraint prediction may be performed when two lanes are merging intoone lane and lane lines are not detected on the side of the lane intowhich a second lane is merging.

As shown, roadway 900 may have lane 902 in which vehicle 200 istraveling. Lane 902 may be defined on a first side by first laneconstraint 904 and on a second side, opposite the first side, by laneconstraint 906. These lane constraints 904 and 906 may be detected bysystem 100, as indicated by the bolded lines showing lane constraints904 and 906. Vehicle 200 may have a first vehicle side 908 on the sameside of vehicle 200 as first lane constraint 904, and a second vehicleside 910 on the same side of vehicle 200 as second lane constraint 906.

Processing unit 110 may be configured to determine first lane constraint904 and second lane constraint 906 based on a plurality of imagesacquired by image capture device 122-126 that processing unit 110 mayreceive via data interface 128. According to some embodiments, firstlane constraint 904 and/or second lane constraint 906 may be identified,detected, estimated, or predicted in any or all of the manners discussedabove with respect to FIGS. 8A-8C.

As shown, roadway 900 may have lane 916. Lane 916 may be bounded by laneconstraints. In the example shown, lane 916 is marked by solid lane line918 on its left side and solid lane line 120 on its right side. In theexample shown, lane 916 merges into lane 902 as roadway 900 continues inthe direction of travel of vehicle 200.

In FIGS. 9A-9C, lane constraint 904 is continuously marked by solid line903 on roadway 900. Lane constraint 906, on the other hand, isinconsistently marked; at the bottom-most portion of the figures, laneconstraint 906 is marked by solid line 905 a on roadway 900; at theportion above that, as lane 916 begins to merge into lane 902, laneconstraint 906 is marked by dashed line 905 b on roadway 900; at theportion above that, as lane 916 finishes merging into lane 902, laneconstraint 906 is not marked; finally, at the top-most portion of thefigures, lane constraint 906 is again marked by a solid line, this timeby solid line 905 c on roadway 900.

In FIGS. 9A-9C, the detection of lane constraints 904, 906, and 922 bysystem 100 is depicted by the bolded lines that overlay the respectivelane lines in front of vehicle 200. As depicted by the bolded lines inFIG. 9A, system 100 detects lane constraint 904 as a solid-line laneconstraint on the left side of vehicle 200 for a certain visibledistance in front of vehicle 200. Similarly, system 100 detects laneconstraint 906 as a solid lane constraint on the right side of vehicle200 for a certain visible distance in front of vehicle 200. Inaccordance with the detection of lane constraints 904 and 906, system100 causes vehicle 200 to travel inside lane 902 as defined by laneconstraints 904 and 906.

FIG. 9B depicts vehicle 200 continuing to progress along roadway 900 inlane 902 following the point in time depicted in FIG. 9A. As vehicle 200reaches the portion of roadway 900 at which lane 916 merges into lane902 from the right side, where lane constraint 906 is marked briefly bydashed line 905 b and then is not marked by any lines, lane constraintprediction may be performed by system 100. As shown, system 100 predictsthe location of lane constraint 906 as being located a distance 912 fromopposite lane constraint 904. This distance 912 may be determined, insome embodiments, based on the historical information detected andstored by system 100 about the previous widths of lane 902 and/or theaverage historical width of lanes in general. As shown, system 100predicts that lane constraint 906 continues to be a dashed laneconstraint, based on the fact that lane constraint 906 was marked bydashed line 905 b at the last time that lane constraint 906 was detectedby system 100 before lane constraint prediction was activated. Inaccordance with the continued detection of lane constraint 904 and theprediction of lane constraint 906, system 100 causes vehicle 200 tocontinue to travel inside lane 902 as defined by lane constraints 904and 906.

Also shown in FIG. 9B is system 100 detecting lane constraint 922, whichbounds the right side of lane 916, as lane 916 merges into lane 902.System 100 detects lane constraint 922 as a solid lane constraint basedon the corresponding solid line 920 on roadway 900. In some embodiments,system 100 may determine that detected lane constraint 922 is not aconstraint of lane 902 in which vehicle 200 is traveling, but rather isa lane constraint of a lane that is merging into lane 902. Thisdetermination may be made, in some embodiments, in accordance with adetermination lane constraint 922 is detected as approaching lane 902 atabove a threshold rate of approach. For example, if a detected laneconstraint outside a constraint of a lane in which the vehicle istraveling makes an angle that is more than a predefined threshold angleabove parallel with a detected or previously detected lane constraint ofthe lane in which the vehicle is travelling, then the outside laneconstraint may be determined to correspond to an approaching merginglane.

In some embodiments, the determination that a lane is merging intoanother lane may be made in accordance with the detection and analysisof other aspects, characteristics, or objects in the roadway in oraround a vehicle. For example, a second vehicle (e.g., different fromthe vehicle in which the system is provided) may be detected andtracked, and the system may determine that the other vehicle istraveling at an angle to a lane on the roadway that is indicative of alane in which the other vehicle is traveling merging into another lane.In some embodiments, the system may detect that the other vehicle isapproaching a lane, from an area outside the lane, at above a thresholdrate of approach. In some embodiments, the system may make such adetermination about one or more other vehicles, and may accordinglydetermine that lanes around the one or more other vehicles are merging.In some embodiments, information about the one or more other vehiclesmay be received via the multiple images captured by cameras associatedwith the system; in some embodiments, alternatively or additionally,information about the one or more other vehicles may be received via anysuitable form of electronic communication, such as communicationtransmitted from one or more of the one or more other vehicles.

In yet some other embodiments, the determination that lanes are mergingmay be made in accordance with historical information about laneconfiguration and/or layout, such as historical information about thelanes at or around the current location of the vehicle.

FIG. 9C depicts vehicle 200 continuing to progress along roadway 900 inlane 902 following FIG. 9B. As vehicle 200 reaches the portion ofroadway 900 at which the merge of lane 916 into lane 902 has beencompleted, lane constraint 906 is once again marked by a solid lines onroadway 900, this time by solid line 905 c, and lane constraintprediction may cease to be performed by system 100, as lane constraint906 is once again detected (rather than predicted) as a solid laneconstraint by system 100. In some embodiments, system 100 may determinethat it is detecting a lane constraint of the lane in which vehicle 200is located (rather than a lane constraint of a merging lane) bydetermining that the lane constraint is no longer approaching thevehicle (or the lane in which the vehicle is traveling) at above thepredefined threshold rate of approach. In some embodiments, system 100may determine that it is detecting a lane constraint of the lane inwhich the vehicle is located (rather than a lane constraint of a merginglane) by determining that the lane constraint is within a predefinedthreshold distance of the predicted location of the lane constraint, aspredicted by lane constraint prediction.

As depicted in FIG. 9C, vehicle 200 has traveled distance 914 whileperforming lane constraint prediction. In some embodiments, laneconstraint prediction may only be performed continuously orsubstantially continuously for a predefined maximum distance (and/orpredefined maximum time). According to some embodiments, the maximumdistance/time that is allocated for lane constraint predication can beset dynamically. For example, the maximum distance/time that isallocated for lane constraint predication can be adjusted according tothe vehicle's surrounding, the speed of the vehicle, the topology of theroad, the brightness or the environment, the weather, the density and/orproximity of other vehicles on the roadway, and/or any other roadconditions or characteristics. In some embodiments, when it isdetermined that an approaching merging lane is present in the vicinityof vehicle 200, system 100 may permit lane constraint prediction to beexecuted for a greater total distance than it would otherwise bepermitted to be executed. For example, while a default maximum distancemay be 18 meters, the maximum distance in situations when an ongoinglane merge is detected may be 1000 meters. This difference may accountfor the fact that lane merges may take several hundred meters to becompleted. In the depicted example of FIG. 8C, distance 914 is less thanthe predefined maximum distance for performing lane constraintprediction in a lane merge situation, such that lane constraintprediction was able to be performed continuously as vehicle 200traversed the distance between its position in FIG. 9B and its positionin FIG. 9C.

FIGS. 10A and 10B illustrate an embodiment in which vehicle 200 travelson roadway 1000. FIG. 10A shows a three-dimensional view as seen fromthe perspective of vehicle 200, such as one of the one or more imagesreceived by system 100 as vehicle 200 travels on roadway 1000. (FIG. 10Adepicts a lead vehicle driving on roadway 1000 in front of vehicle 200,not to be confused with vehicle 200 itself.) FIG. 10B shows an overheaddiagram of vehicle 200 on the same portion of roadway 1000.

In the depicted example, the disclosed systems and methods for laneconstraint prediction (e.g., predicting the location of a laneconstraint when guard rail shadow conditions are present) and operatingvehicle 200 within a lane defined by a predicted lane constraint may beused. As shown, roadway 1000 may have lane 1002 in which vehicle 200 istraveling. Lane 1002 may be defined on a first side by first laneconstraint 1004 and on a second side, opposite the first side, by laneconstraint 1006. These lane constraints 1004 and 1006 may, in someembodiments, be detected by system 100, as indicated by the bolded linesshowing lane constraints 1004 and 1006. Vehicle 200 may have a firstvehicle side 1008 on the same side of vehicle 200 as first laneconstraint 1004, and a second vehicle side 1010 on the same side ofvehicle 200 as second lane constraint 1006.

Roadway 1000 may include lane lines 1003 a and 1003 b, which physicallymark the left and right sides, respectively, of lane 1002 on roadway1000.

Roadway 1000 may include guardrail 1012, which is supported by posts1014. Both guard rail 1012 and its posts 1014 are casting guard railshadow 1016, which in the depicted example falls over lane line 1003 aand into lane 1002.

As depicted by the bolded lines in FIGS. 10A and 10B, system 100 detectslane constraint 1006 as a solid-line constraint on the right side ofvehicle 200 for a certain visible distance in front of vehicle 200. Onthe left side of roadway 1000, the bolded lines show how system 1000detects several edges, one of which is attributable to lane line 1003 aand two of which (shadow edges 1005 a and 1005 b) are attributable toguard rail shadow 1016. In some embodiments, the detection of multipleedges on one side of a vehicle or lane may make it more difficult toidentify a lane constraint from the images available to system 100, suchthat the lane constraint cannot be identified with a high level ofcertainty. Accordingly, in some embodiments, when system 100 determinesthat guard rail 1012 or guard rail posts 1014 are present, that guardrail shadow 1016 is being cast onto roadway 1000, or that multiple edgesare detected on the same side of lane 1002, system 100 may determinethat guard rail shadow conditions are present, and may accordinglyengage a guard rail shadow mode. In some embodiment, a guard rail shadowmode may cause lane constraint prediction to be executed for the side ofa lane that is proximate to the detected guard rail, shadow, or edges.By using lane constraint prediction, system 100 may predict the locationof lane constraint 1004 in accordance with any of the techniquesdiscussed above with respect to FIGS. 8A-8C and/or 9A-9C. In thedepicted example, system 100 correctly predicts the location of laneconstraint 1002 to coincide with the edge defined by painted lane line1003 a, rather than either of the edges defined by guard rail shadow1016. In accordance with the detection of lane constraints 1004 and1006, system 100 causes vehicle 200 to travel inside lane 1002 asdefined by lane constraints 1004 and 1006.

In some embodiments, guard rail shadow prediction may cause laneconstraint prediction to be engaged with no limitation as to how longlane constraint prediction can be carried out. Thus, for example, ratherthan the maximum distance for lane constraint prediction being set to 18meters (as in the example of FIGS. 8A-8C) or 1000 meters (as in theexample of FIGS. 9A-9C), system 100 may turn off the maximum distance,functionally configuring the system so that lane constraint predictionmay continue for an indefinite distance (and/or an indefinite time) solong as guard-rail shadow conditions continue to be detected and so longas no lane constraint prediction cancellation conditions are detected.

FIG. 11 is an exemplary block diagram of memory 140 or 150, which maystore instructions for performing one or more operations consistent withdisclosed embodiments. As illustrated in FIG. 11, memory 140 or 150 maystore one or more modules for performing the techniques describedherein. For example, memory 140 or 150 may store image obtaining module1100, roadway lane constraint recognizing module 1102, determiningmodule 1104, roadway lane constraint predicting module 1106, vehicleenabling module 1108, and lane constraint prediction ceasing module1110. As would be appreciated by a person of ordinary skill in the art,any of the previously recited modules may be combined with each other orwith other modules, interfaced with one another or with other modules,and/or subdivided into further modules and/or sub-modules.

In some embodiments, processing unit 110 may execute instructions storedon the one or more modules in memory 140 or 150, and the execution byprocessing unit 110 may cause system 100 to perform the methodsdiscussed herein.

In some embodiments, processing unit 110 may cause system 100 to obtain(e.g., with image obtaining module 1100) multiple images from a camera(e.g., image capture devices 122, 124, and/or 126), wherein the camerais adapted to capture, when mounted on a vehicle (e.g., vehicle 200) andwhile the vehicle is in motion, images of a roadway within a field ofview of the camera, apply image processing to the multiple images torecognize (e.g., with roadway lane constraint recognizing module 1102) afirst roadway lane constraint, determine (e.g., with determining module1104), based on the multiple images, that one or more defined laneprediction conditions are satisfied, in accordance with the adetermination that one or more defined lane prediction conditions aresatisfied, predict (e.g., with roadway lane constraint predicting module1106) the location of a second roadway lane constraint, and issue (e.g.,with issuing module 1108) a notification or a control signal to controla motion of the vehicle according to the predicted location of thesecond roadway lane constraint.

In some embodiments, processing unit 110 may cause system 100 to issue(e.g., with issuing module 1108) a notification or a control signal todirect a motion of the vehicle according to a location of the firstroadway lane constraint.

In some embodiments, processing unit 110 may cause system 100 to issue(e.g., with issuing module 1108) a notification or a control signal todirect a motion of the vehicle along a lane or an area between the firstroadway lane constraint and the second roadway lane constrain.

In some embodiments, processing unit 110 may cause system 100 todetermine (e.g., with determining module 1104) whether one or moredefined lane prediction cancellation conditions are satisfied, and inaccordance with a determination that one or more defined lane predictioncancellation conditions are satisfied, cease to predict (e.g., with laneconstraint prediction ceasing module 1110) the location of the secondroadway lane constraint.

FIG. 12 illustrates a process 1200 for implementing lane constraintprediction consistent with the disclosed embodiments. In someembodiments, the steps in process 1200 may be illustrated by thesituations illustrated in FIGS. 8A-8C, 9A-9C, and 10A-10B. According tosome embodiments, process 1200 may be implemented by one or morecomponents of navigation system 100, such as at least one processingunit 110. According to some embodiments, process 1200 may be implementedby system 100 associated with vehicle 200.

At step 1202, in some embodiments, a system receives multiple images ofa roadway in the vicinity of a moving vehicle associated with thesystem. In some embodiments, the one or more images are acquired usingat least one image capture device, such as image capture devices 122,124, and/or 126. The plurality of images may be received by the systemthrough a data interface such as data interface 128. In the exampleillustrated in FIGS. 8A-8C, system 100 may acquire a plurality of imagesof roadway 800 in the vicinity of vehicle 200.

At step 1204, in some embodiments, the system recognizes a first roadwaylane constraint. In some embodiments, the system may use image analysistechniques to process the one or more images received to detect laneconstraints by identifying objects, markings, or other characteristicsin the images that are indicative of a lane constraint, such as lanemarkings, roadway edges, lane dividers, and/or other vehicles. In someembodiments, the system may attempt to identify at least one laneconstraint on one side of the vehicle and another lane constraint on theother side of the vehicle (e.g., one lane constraint to each side of thecenter line of the vehicle in its traveling direction, front to back).In some embodiments, the system may attempt to identify at least onelane constraint on one side of a lane and another lane constraint onanother side of the lane. In the example illustrated in FIGS. 8A-8C,system 100 may analyze the images received of roadway 800, and maydetect lane constraint 804 on the left side of lane 802 as correspondingto and coinciding with solid lane line 803 a. That is, in someembodiments, lane line 803 a may be recognized by system 100 in themultiple acquired images, and system 100 may determine that lane line803 a is a visual marker for lane constraint 804.

At step 1206, in some embodiments, the system determines, based on themultiple images, whether one or more defined lane prediction conditionsare satisfied. In some embodiments, a lane prediction condition may beany predetermined condition by which system 100 may determine whether topredict the location of a lane constraint. In some embodiments, only onelane prediction condition need be satisfied, while in other embodiments,a minimum threshold number of lane prediction conditions (e.g., two ormore) need to be satisfied. In some embodiments, the minimum thresholdnumber of lane predication conditions can be determined in accordancewith historical information regarding the roadway lane layout, theroadway topology, shadow conditions, visibility conditions, presence ofguard rails and/or other barriers along the roadway, presence of othervehicles on the roadway, etc.

In some embodiments, the one or more defined lane prediction conditionscomprise that the second roadway lane constraint ceases to berecognized, based on the multiple images, as the vehicle progresses. Forexample, in some embodiments, the system may continuously capture andconsult multiple images corresponding to the vehicle's current locationand vicinity; as the vehicle travels along a roadway and along a lane, alane constraint that was recognizable at one instance in time based onone subset of the multiple images may cease to be recognizable at asubsequent instance in time based on a later subset of the multipleimages. In the example of FIGS. 8A-8C, system 100 may analyze the imagesreceived of roadway 800 as vehicle 200 travels along roadway 800 fromits position in FIG. 8A to its position in FIG. 8B. As vehicle 200travels from its position in FIG. 8A to its position in FIG. 8B, itreaches the middle portion of roadway 800 at which dotted lane line 803b is not painted on roadway 800. Upon analyzing the received multipleimages from this middle portion of roadway 800, system 100 may cease torecognize lane constraint 806 due to the absence of lane line 803 b.This ceasing to recognize a lane constraint as vehicle 200 progressesalong roadway 800 may constitute a lane prediction condition in someembodiments.

In some embodiments, the one or more defined lane prediction conditionscomprise that the lane constraint on the first side of the lane has beenrecognized for at least a threshold time or a threshold distance.Optionally, a threshold time or a threshold distance associated with theone or more defined lane prediction conditions can be dynamic and can beadjusted according to historical information regarding the roadway lanelayout, the roadway topology, shadow conditions, visibility conditions,presence of guard rails or other barriers along the roadway, presence ofother vehicles on the roadway, etc. In some embodiments, such a laneprediction condition may serve to prevent the system from (or reduce thelikelihood of the system) engaging in lane constraint prediction whenthe system detects a lane constraint on a single side of a lane (or asingle side of a vehicle) for a first time. That is, in someembodiments, if a vehicle is traveling and comes upon a single lane lineor other lane marking or indicator for the first time, the system mayrefrain from predicting the location of another lane constraint based onthe single lane marking/indicator. In some embodiments, this laneprediction condition may serve to ensure (or increase the likelihood)that the system is making valid and accurate lane constraint predictionsbased on actual lanes that exist on a roadway, and not on isolated,stray, or erroneously or randomly placed lane markings/indicators. Insome embodiments, a predefined threshold distance may be fixed in thesystem, or may be determined by the system based on a current speed ofthe vehicle, the size or posted speed of the roadway on which thevehicle is traveling, or any other discernible variables regarding thevehicle or roadway.

In some embodiments, the one or more defined lane prediction conditionscomprise that a guard rail is present in the vicinity of the vehicle. Insome embodiments, the system may engage in lane constraint predictionwhen the system determines that the vehicle is in the vicinity of aguard rail. In some embodiments, this lane prediction condition mayserve to prevent the system from erroneously determining that one ormore edges detected in the one or more images correspond to a laneconstraint, when in reality the one or more edges is associated with aguard rail or with a guard rail shadow. In some embodiments, a guardrail may cast a shadow onto a roadway, and an edge of the shadow on theroadway may be detected by the image analysis techniques used by thesystem. In some embodiments, the system may erroneously determine thatthe guard rail shadow is a marker of a lane constraint, and may causethe vehicle to travel in accordance with the guard rail shadow ratherthan with the actual lane constraint. In some embodiments, in order toprevent this error, the system may recognize that a guard rail ispresent in the vicinity of the vehicle, and may therefore engage in laneconstraint prediction to predict the location of a lane constraint onthe side of a lane with a guard rail, rather than attempting torecognize the lane constraint on that side of the lane and riskingerroneously recognizing the guard rail shadow as a lane constraint.

In the example of FIGS. 10A and 10B, vehicle 200 (shown in FIG. 10B; notshown in FIG. 10A, as vehicle 200 constitutes the point of view in FIG.10A) is traveling on roadway 1000 in lane 1002, which has guard rail1012 to the left side of lane 1002. In some embodiments, system 100 mayanalyze the one or more images to detect that guard rail 1012 is presentin the vicinity of vehicle 200 on the left side of lane 1002. In someembodiments, the presence of guard rail 1012 may satisfy a laneprediction condition.

In some embodiments, determining that a guard rail is present in thevicinity of the vehicle comprises detecting, in the one or more images,a pattern created by posts supporting the guard rail. In someembodiments, the system may be configured to detect one or more patternsin the one or more images that is known or suspected to be indicative ofthe presence of a guard rail. One such pattern may, in some embodiments,be referred to as a “rapid dash” pattern, and may be present when thesystem detects in the one or more images a pattern of rapid successivedashes or rapid successive lines caused by the posts supporting a guardrail. In some embodiments, the system may be configured to detect anyother suitable pattern or visual indicia known to be characteristic ofthe posts that support guard rails. In some embodiments, the system maydetect a pattern of the posts of a guard rail, while in some embodimentsthe system may alternatively or additionally detect a pattern of theshadows cast by the posts of a guard rail. In the example of FIGS. 10Aand 10B, guard rail 1012 is supported by posts 1014 (shown in FIG. 10A;not visible from the overhead angle of FIG. 10B). In FIGS. 10A and 10B,posts 1014 are casting a shadow that falls onto roadway 1000 and intolane 1002. System 100 may detect the presence of a rapid dashed patternor any other suitable pattern created in the multiple images by posts1014 or their shadows, and this detection may be one manner in whichsystem 100 determines that a guard rail is present in the vicinity ofvehicle 200.

In some embodiments, system 100 may utilize depth information toidentify a shape that is characteristic of a guard rail or of a portionof a guard rail. It would be appreciated that a painted line would havesubstantially no volume, and its height is virtually zero relative tothe roadway surface, and so, depth information can be useful todistinguish between a lane marking and a guard rail.

In some embodiments, the one or more defined lane prediction conditionscomprise that a guard rail is casting a shadow onto the roadway. In someembodiments, a lane prediction condition may be based on the detectionby the system of a shadow of a guard rail, in place of or in addition todetection by the system of a guard rail itself. In some embodiments, alane prediction condition may be satisfied if a guard rail shadow isdetected anywhere in the vicinity of the vehicle; in some embodiments, alane prediction condition may be satisfied if a shadow is detected asbeing cast from a guard rail in the direction of a lane or a roadway inor on which the vehicle is traveling; in some embodiments, a laneprediction condition may be satisfied if a shadow is detected as beingcast from a guard rail onto a roadway or lane in which the vehicle istraveling.

In some embodiments, determining that a guard rail is casting a shadowonto the roadway may involve determining whether a predicted location ofthe sun comports with the location of a detected edge that is possible aguard rail shadow. For example, in some embodiments, the system maydetermine a position of the sun and an orientation of the sun withrespect to the multiple images available to the system. Thesedeterminations as to the location of the sun and the orientation of thevehicle may be made in accordance with various pieces of informationincluding, for example, one or more of the multiple images, a locationand/or orientation of the vehicle determined by a GPS, an orientation ofthe vehicle determined by a compass, and/or a time of day. The systemmay determine, based on the determined location of the sun andorientation of the vehicle that a shadow is predicted to be cast from aguard rail in a certain direction at a certain time.

In the example of FIGS. 10A and 10B, guard rail 1012 is casting shadow1016 onto roadway 1000 and into lane 1002; shadow 1016 falling onroadway 1000 and in lane 1002 may be detected by system 100, and saiddetection may satisfy a lane prediction condition.

In some embodiments, the one or more defined lane prediction conditionscomprise that multiple substantially parallel edges are detected, in themultiple images, on the roadway on one side of a lane on the roadway. Insome embodiments, image analysis techniques applied by the system to themultiple images received may detect edges in one or more of the multipleimages received; in some embodiments, the detection of edges may be usedto recognize objects and markings in the environment around the vehicle.In some embodiments, the detection of edges in the multiple images maybe used to recognize lane lines or markings or the edges or roadways,and to thereby determine the location of lane constraints. In someembodiments, including those in which a guard rail shadow is cast onto aroadway or lane in which the vehicle is traveling, the system may detectthe edge of a guard rail shadow as an edge in the multiple images. Insome other embodiments, other objects or markings may cause the systemto detect multiple edges on the same side of one lane. In theseembodiments, determination of which edge corresponds to the correct laneconstraint may be difficult, slow, or unreliable. For this reason, itmay be advantageous in some embodiments to refrain from attempting todetermine, based on the multiple images, which of several detected edgescorresponds to a lane constraint, and instead to simply predict thelocation of the lane constraint using lane constraint prediction whenmultiple edges are detected in the multiple images as discussed above.In the example of FIGS. 10A and 10B, system 100 detects, in the multipleimages, three different edges on the left side of lane 1002, indicatedby the bolded lines marked 1004, 1005 a, and 1005 b. In the example ofFIGS. 10A and 10B, as explained above, system 100 may determine thatmultiple edges are detected on the left side of lane 1002, and thisdetection may constitute a lane prediction condition.

In some embodiments, if it is determined, at block 1206, that one ormore defined lane prediction conditions are not satisfied, then method1200 may return to step 1202 and may continue to acquire additionalimages of the roadway as time progresses.

In some embodiments, if it is determined, at block 1206, that one ormore defined lane prediction conditions are satisfied, then method 1200may proceed to step 1208. At step 1208, in some embodiments, inaccordance with a determination that one or more defined lane predictionconditions are satisfied, the system predicts the location of a secondroadway lane constraint. In some embodiments, the system may predict alane constraint on the opposite side (the vehicle being a reference asto direction of the opposite side) of a lane from a detected laneconstraint. Thus, in some embodiments, if one lane constraint isrecognizable from lane lines or other visible indicia in the receivedimages, and lane prediction conditions such as any of the conditionsdiscussed above are determined to be satisfied, then the system maypredict the location of the second lane constraint on the opposite sideof the lane from the first lane constraint.

In some embodiments, the predicted lane constraint may be predicted toextend for a predetermined distance along the roadway. In someembodiments, the predicted lane constraint may be predicted to beparallel to the recognized lane constraint opposite it. In someembodiments, the predicted lane constraint may be predicted to continuealong the same angle, path, or location as the same lane constraint waspreviously detected by the system. In some embodiments, the predictedlane constraint may be predicted based on the location of another laneconstraint, based on the location of other discernible features of aroadway, or based on the location of the vehicle associated with thesystem and/or on surrounding vehicles. In some embodiments, thepredicted lane constraint may be predicted as being located apredetermined distance from a recognized lane constraint or from anyother object recognized or predicted based on the multiple images of theroadway. In some embodiments, the predicted lane constraint may bepredicted to be a lane constraint type that matches or is determined inaccordance with other lane constraint types detected in the multipleimages, or determined in accordance with a lane constraint typedetermined for the predicted lane constraint based on previous images ofthe roadway from a previous time (e.g., if a system detects a solidsingle white line on the right side of the vehicle, then ceases todetect any lane line, the system may predict that a solid single whitelane constraint continues on the right side of the vehicle; alternatelyor additionally, a system may predict that a solid single white line islocated on the opposite side of a lane from a solid single yellow line).

In the example of FIGS. 8A-8C, system 100 recognizes lane constraint 804based on the detection of lane line 803 a in the multiple images. Whenvehicle 200 reaches its position in FIG. 8B and does not detect any lanelines on the right side of lane 802, then a lane prediction conditionmay be satisfied. Accordingly, in some embodiments, system 100 maypredict lane constraint 806 on the right side of lane 802, as shown inFIG. 8B. In some embodiments, lane constraint 806 may be predicted as asingle dashed line lane constraint, based on the previous recognition,in FIG. 8A based on lane lines 803 b, of lane constraint 806 as a singledashed line lane constraint. In some embodiments, the position, angle,type, and/or any other characteristic of lane constraint 806 may bepredicted based on any stored or otherwise determined informationaccessible by system 100 about vehicle 200, roadway 800, or any othersurrounding environmental factors.

In some embodiments, predicting the location of a lane constraintcomprises predicting a width of a lane based on information about apreviously determined width of the lane. In some embodiments, a width ofa lane may be predicted based on historical information about lane widthor cached information about the width of a specific lane in the recentpast (e.g., a few seconds before lane constraint prediction wasperformed). In some embodiments, when a system is detecting bothconstraints of a lane and may thereby determine the width of that lane,and the system then ceases to detect or recognize one of the laneconstraints, then the system may predict that the lane continues to bethe same width. The system may accordingly predict the location of thepredicted lane constraint such that the lane is predicted to remainconstant or substantially constant in width. In the example of FIGS.8A-8C, when system 100 is predicting the location of lane constraint 806in FIG. 86, constraint 806 is predicted as being a distance 812 fromlane constraint 804. This distance 812 may be the same distance 812 thatwas detected between lane constraint 806 and lane constraint 804 whenvehicle 200 was in the position shown in FIG. 8A. System 100 may, insome embodiments, log or cache historical information about lane width,such that it can quickly recall lane width information in order topredict the location of a predicted lane constraint.

At step 1210, in some embodiments, the system enables the moving vehicleto avoid the first roadway lane constraint and the second roadway laneconstraint. Thus, in some embodiments, the system may cause the vehicleto travel in a lane defined on one side by a detected/recognized laneconstraint and on the opposite side by a predicted lane constraint. Insome embodiments, the system may cause any suitable steering,accelerating, decelerating, or other control of the vehicle to cause itto travel within the lane defined by the predicted and detected laneconstraints. In the example of FIGS. 8A-8C, when system 100 detects laneconstraint 806 in FIG. 8B, system 100 then causes vehicle 200 to travelalong lane 802 between detected lane constraint 804 and predicted laneconstraint 806 to travel from its position in FIG. 8B to its position inFIG. 8C.

At step 1212, in some embodiments, the system determines whether one ormore defined lane prediction cancellation conditions are satisfied. Insome embodiments, a lane prediction cancellation condition may be anypredetermined condition by which the system may determine whether tocease predicting the location of a lane constraint. In some embodiments,the location of a lane constraint may continue to be predicted once thesystem begins predicting the location of the lane constraint until oneor more predefined lane prediction cancellation conditions aresatisfied. In some embodiments, only one lane prediction cancellationcondition need be satisfied in order for lane prediction to cease. Insome embodiments, a predefined number or lane prediction conditionsgreater than one must be satisfied in order for lane prediction to beceased. Optionally, a confidence score may be assigned to a laneprediction condition according to predefined criteria. Optionally, somelane prediction condition can have a greater weight than others. Stillfurther by way of example, an overall score can be computed to determinewhether a lane prediction cancellation condition is met, and such acalculation can take into account confidence scores and weights whichwere assigned to lane prediction conditions.

In some embodiments, the one or more defined lane predictioncancellation conditions comprise that, after ceasing to be recognized,the second lane constraint is recognized based on the one or moreimages. For example, in some embodiments, as the vehicle is travelingalong a roadway, a lane constraint may cease to be recognized (asdiscussed above), such as when a roadway is unmarked or lane lines arenot detectable in the multiple images; lane prediction may beaccordingly performed, as discussed above. In some embodiments,thereafter, for example as the vehicle continues to travel along theroadway, the system may once again recognize the lane constraint in themultiple images as the system continues to acquire new images. Forexample, if a vehicle reaches a portion of the roadway where the lanelines are no longer missing, or if the lane lines otherwise becomedetectable by the system once more, the system may recognize the laneconstraint based on the updated images of the roadway. This recognitionof the second lane constraint—the lane constraint that was beingpredicted—may in some embodiments constitute a lane predictioncancellation condition that may cause the system to cease to predict thelocation of the second lane constraint and to instead return to relianceon the detected/recognized location of the lane constraint based on themultiple images.

In the example of FIGS. 8A-8C, while system 100 may be predicting thelocation of lane constraint 806 when vehicle 200 is located at theposition shown in FIG. 8B, as vehicle 200 continues to the positionshown in FIG. 8C, the dashed lane lines 803 b on the upper half of thediagram may once again be visible in the images acquired by system 100.Based on these new images, system 100 may once again detect/recognizethe presence of lane constraint 806 based on lane line 803 b, and thisrecognition may constitute a lane prediction cancellation condition thattriggers the cessation of the prediction of the location of laneconstraint 806.

In some embodiments, the one or more defined lane predictioncancellation conditions comprise that a driver is moving the vehiclebetween lanes. In some embodiments, the system may determine based onthe multiple images that the driver is steering or merging the vehicleto another lane. In some embodiments, this detection may satisfy a laneprediction cancellation condition and may trigger the cessation of laneconstraint location prediction.

In some embodiments, the one or more defined lane predictioncancellation conditions comprise that the vehicle is traversing a curvethat exceeds a predefined sharpness threshold. In some embodiments, thesystem may access a predefined or dynamically determined (e.g.,determined in accordance with vehicle speed, posted speed limit, roadconditions, weather conditions, environment brightness, the vicinity ofother vehicles, etc.) sharpness threshold for curves, and may determinewhether a curve in a roadway or a lane exceeds the sharpness threshold(e.g., is sharper than the maximum permissible sharpness). If it isdetermined that a curve being traversed or approached by the vehicleexceeds the maximum permissible sharpness, then the system may determinethat a lane prediction cancellation condition is satisfied, and mayaccordingly cease lane constraint prediction.

In some embodiments, determining whether one or more defined laneprediction cancellation conditions are satisfied comprises selecting amaximum lane prediction distance setting, and determining whether adistance traveled by the vehicle while predicting the location of thesecond roadway lane constraint is greater than a maximum lane predictiondistance associated with the maximum lane prediction distance setting.In some embodiments, a maximum lane prediction distance may bepredefined, set, or dynamically determined by the system. In someembodiments, the system may monitor the distance traveled by the vehiclewhile the system is predicting the location of a lane constraint, andmay cease to predict the location of the lane constraint if the distancetraveled exceeds the maximum permissible lane prediction distance. Insome embodiments, implementation of a maximum permissible laneprediction distance may improve safety and functionality by ensuringthat the system does not predict the location of a lane constraint foran overly-long period of time or over an overly-long distance, overwhich distance or time one or more characteristics of the lane or theroadway may change or vary. As characteristics of lanes and roadways canbe expected, in some embodiments, to change or vary over space and time,the system may improve safety by ensuring that lane constraint referencepoints or indicators, such as lane lines or other lane markers, aredetected based on the multiple images acquired by the system at leastonce over a maximum lane prediction distance (or, in some embodiments, amaximum lane prediction time).

In some embodiments, the maximum lane prediction distance may be variedby the system in different situations. In some embodiments, a maximumlane prediction distance may be associated with one or more maximum laneprediction distance settings, such that the system may select onemaximum lane prediction distance setting and implement the associatedmaximum lane prediction distance. In some embodiments, different maximumlane prediction distance settings may be implemented in differentdriving scenarios, as detected and determined by the system.

In some embodiments, a standard or default maximum lane predictiondistance setting may be associated with a standard or default maximumlane prediction distance. In some embodiments, the standard or defaultmaximum lane prediction distance may be 18 meters.

In the example of FIGS. 8A-8C, vehicle 200 has traveled distance 814from its position in FIG. 8B, at which point lane constraint predictionwas commenced, to its position in FIG. 8C, at which point laneprediction is ceased. This distance 814 may be less than a standard ordefault maximum lane prediction distance of 18 meters, such that laneconstraint prediction may have been performed continuously between thepositions of vehicle 200 in FIGS. 8B and 8C.

In some embodiments, selecting a maximum lane prediction distancesetting comprises, in accordance with a determination that a first laneis merging with a second lane in the vicinity of the vehicle, selectinga maximum lane prediction distance setting such that the maximum laneprediction distance is greater than a default maximum lane predictiondistance. In some embodiments, the system may thus implement an extendedor increased maximum lane prediction distance. For example, in someembodiments, the system may determine, based on the multiple images,that a lane is merging with the lane in which the vehicle is traveling,or that the lane in which the vehicle is traveling is merging withanother lane. The system may determine that a lane merge is occurring inthe vicinity of the vehicle by detecting a lane constraint (e.g., byrecognizing a lane line) on the far side of the merging lane adjacent tothe vehicle's lane, and by determining that the lane constraint on theopposite side of the adjacent lane is approaching the lane in which thevehicle is traveling at above a predefined or dynamically determinedthreshold rate of approach. That is, in some embodiments, the system mayrecognize the far side of an adjacent lane approaching the lane in whichthe vehicle is traveling as the vehicle progresses along a roadway, andmay accordingly determine that the adjacent lane is merging with thelane in which the vehicle is traveling.

In some embodiments, roadways may be expected to feature long distancesof unmarked lane constraints when two lanes are merging with oneanother, so it may be advantageous for the system to be configured topredict the location of a lane constraint for a greater distance and/ora greater time than a default maximum lane prediction distance or time.In some embodiments, the increased maximum lane prediction distanceassociated with lane merge situations may be 1000 meters.

In the example of FIGS. 9A-9C, vehicle 200 has traveled distance 914from its position in FIG. 9B, at which point lane constraint predictionwas commenced, to its position in FIG. 9C, at which point laneprediction is ceased. This distance 914 may be greater than a standardor default maximum lane prediction distance, such that lane constraintprediction may have been ceased before vehicle 200 reached its positionin FIG. 9C if the default maximum lane prediction distance setting wasselected. However, as indicated by the bolded line in FIG. 8B, system100 recognized lane constraint 922 on the right side of lane 916approaching lane 902 as vehicle 200 progressed, and may have accordinglydetermined that the lanes were merging and that a maximum laneprediction distance setting associated with lane merges should beselected. The selected setting may be associated with a maximum laneprediction distance of 1000 meters, greater than the default maximumlane prediction distance of 18 meters. Thus, in some embodiments,distance 914 may be less than the increased maximum lane predictiondistance of 1000 meters, such that lane constraint prediction may havebeen performed continuously between the positions of vehicle 200 inFIGS. 9B and 9C.

In some embodiments, selecting a maximum lane prediction distancesetting comprises, in accordance with a determination that definedguard-rail conditions are satisfied, selecting a maximum lane predictiondistance setting having no maximum lane prediction distance. That is, insome embodiments, when system 100 detects that guard-rail conditions aresatisfied, system 100 may predict the location of a lane constraintindefinitely, without regard for any maximum lane prediction distance.In this way, in some embodiments, the maximum lane prediction distanceassociated with guard-rail conditions may be thought of as an infinitemaximum distance, or as a non-existent maximum distance.

In some embodiments, guard-rail conditions may be satisfied in anysituation where the system detects the presence of one or more guardrails, guard rail supports, and/or guard rail shadows in the vicinity ofthe vehicle. For example, guard-rail conditions may be satisfied in anyor all of the situations discussed above in which the system maydetermine that a guard rail and/or a guard rail shadow are present inthe vicinity of the vehicle and/or on a roadway or lane in which thevehicle is traveling. In some situations, guard-rail conditions may besatisfied when the presence, location, and/or orientation of a guardrail and/or guard rail shadow causes the system to engage in laneconstraint prediction, as discussed above.

In some embodiments, it may be advantageous for no maximum laneprediction distance (or an infinite maximum lane prediction distance) tobe applied in situations in which a guard rail and/or guard-rail shadowis present, because guard rails and their associated shadows can beexpected to persist continuously for very long distances along roadways(often greater than a default maximum lane prediction distance or evenan increased lane prediction distance associated with lane merges). Inthe example of FIGS. 10A and 10B, when system 100 begins predicting thelocation of lane constraint 1004 due to the detection of guard railshadow 1016, system 100 may select a maximum lane prediction distancesetting associated with guard rail conditions; the selection of amaximum lane prediction distance setting associated with guard railconditions may cause the system to perform lane prediction of laneconstraint 1004 without being subject to any maximum lane predictiondistance.

In some embodiments, if it is determined, at block 1212, that one ormore defined lane prediction cancellation conditions are not satisfied,then method 1200 may return to step 1202 and may continue to acquireadditional images of the roadway as time progresses.

In some embodiments, if it is determined, at block 1212, that one ormore defined lane prediction cancellation conditions are satisfied, thenmethod 1200 may proceed to step 1214. At step 1212, in some embodiments,in accordance with a determination that one or more lane predictioncancellation conditions are satisfied, the system ceases to predict thelocation of the second lane constraint. In the example of FIGS. 8A-8C,when vehicle 200 reaches its position depicted in FIG. 8C, a laneprediction cancellation condition may be satisfied by the recognition oflane constraint 806 based on lane line 803 b, and system 100 maytherefore cease to predict the location of lane constraint 806. Instead,system 100 may resume determining the position of lane constraint 806based on the detected location of lane line 803 b, as it did previouslyin FIG. 8A.

Construction Zone Detection and Response

System 100 may provide vehicle 200 with construction zone detection,such that the system recognizes the presence of construction zones onroadways and takes action in accordance therewith. In some embodiments,system 100 may recognize various elements in the one or more images thatit receives and may determine, in accordance with the recognition ofthose one or more elements, that vehicle 200 is approaching or inside aconstruction zone. In some embodiments, any automated or driver-assistaction may be taken in accordance with the detection of a constructionzone, including but not limited to changing the speed of vehicle 200,steering vehicle 200, engaging or disengaging one or more lights ofvehicle 200, etc. In some embodiments, in accordance with the detectionof a construction zone, a signal may be outputted by system 100 thatindicates that a construction zone has been detected and/or that aconstruction zone mode is activated. In some embodiments, in accordancewith the detection of a construction zone (and/or in accordance with thedetection of a complex construction zone), automated or driver-assistcontrol of vehicle 200 by system 100 may be disabled, such that controlof vehicle 200 may be returned to the driver.

FIG. 13 illustrates an embodiment in which vehicle 200 is traveling onroadway 1300 and approaching a construction zone. Roadway 1300 has twolanes 1302 and 1304. Lane 1302 is defined by lane constraints 1306 and1308, while lane 1304 is defined by lane constraints 1310 and 1312.These lane constraints are detected by system 100, as shown by thebolded lines indicating lane constraints 1306, 1308, 1310, and 1312.Lane constraints 1306 and 1308 are marked on roadway 1300 by paintedlane lines 1320 and 1322, respectively; while lane constraints 1310 and1312 are marked by on roadway 1300 by painted lane lines 1322 and 1324,respectively.

Roadway 1300 includes several indicators of a construction zone orconstruction area, including painted construction zone lane line 1326.In some embodiments, construction zone lane lines may be painted in adifferent color and/or different style than conventional lane lines. Forexample, in Germany, yellow lane lines are painted on the road toindicate a construction zone, while conventional lane lines are paintedin white. As indicated by the bolded line, system 100 detectsconstruction zone lane constraint 1314 based on painted constructionzone lane line 1326. In accordance with the detection of constructionzone lane constraint 1314, system 100 may cause vehicle 200 to drivewithin the lane defined by construction zone lane constraint 1314 ratherthan the conventional lane constraints. In some embodiments, when system100 has detected that vehicle 200 is in a construction zone,construction zone lane lines may be given precedence over conventionallane lines.

Roadway 1300 may include additional construction zone indicators,including cones 1316, some of which are located on the surface ofroadway 1300 and inside lane 1304. In response to detecting cones 1316,system 100 may cause vehicle 200 to alter its path so as to avoid cones1316, changing a position in a lane of travel or changing lanes ifnecessary. In response to detecting cones 1316, system 100 may determinethat vehicle 200 is in or near a construction zone; activate aconstruction zone mode; output a construction zone signal; disableautomated or driver-assist control of vehicle 200; and/or controlvehicle 200 by changing its speed, steering it, or taking any otherappropriate action (such as to avoid cones 1316).

Roadway 1300 may include additional construction zone indicators,including construction road signs 1318 a and 1318 b, which may indicatethe presence of a construction zone and/or indicate an instruction fordrivers to follow while in the construction zone (e.g., slow, stop,merge, turn, etc.). System 100 may detect construction road signs 1318 aand 1318 b through the manner in which the system may detect any or allother traffic signs, and may take any appropriate action in response todetection of construction road signs 1318 a and 1318 b, includingdetermining that vehicle 200 is in or near a construction zone;activating a construction zone mode; outputting a construction zonesignal; disabling automated or driver-assist control of vehicle 200;and/or controlling vehicle 200 by changing its speed, steering it, ortaking any other appropriate action (such as in accordance with aninstruction on a construction road sign).

FIG. 14 is an exemplary block diagram of memory 140 or 150, which maystore instructions for performing one or more operations consistent withdisclosed embodiments. As illustrated in FIG. 14, memory 140 or 150 maystore one or more modules for performing construction-zone detection asdescribed herein. For example, memory 140 or 150 may store imageobtaining module 1400, construction zone indicator recognizing module1402, determining module 1404, signal outputting module 1406, vehiclemoving enabling module 1408, and vehicle control disabling module 1410.As would be appreciated by a person of ordinary skill in the art, any ofthe previously recited modules may be combined with each other or withother modules, interfaced with one another or with other modules, and/orsubdivided into further modules and/or sub-modules.

In some embodiments, processing unit 110 may execute instructions storedon the one or more modules in memory 140 or 150, and the execution byprocessing unit 110 may cause system 100 to perform the methodsdiscussed herein.

In some embodiments, processing unit 110 may cause system 100 to obtain(e.g., with image obtaining module 1400) multiple images from a camera(e.g., image capture devices 122, 124, and/or 126), wherein the camerais adapted to capture, when mounted on a vehicle (e.g., vehicle 200),and while the vehicle is in motion, images of a roadway within a fieldof view of the camera, apply image processing to the multiple images torecognize (e.g., with construction zone indicator recognizing module1402) one or more indicators of a construction zone, and, in accordancewith recognizing the one or more indicators of a construction zone,output (e.g., with signal outputting module 1406) a signal indicatingthat the vehicle is proximate to a construction zone.

In some embodiments, processing unit 110 may cause system 100 todetermine (e.g., with determining module 1404), based on the one or moreindicators, that the vehicle is proximate to a construction zone, and inaccordance with a determination that the vehicle is proximate to aconstruction zone, output (e.g., with signal outputting module 1406) thesignal indicating that the vehicle is proximate to a construction zone.

In some embodiments processing unit 110 may cause system 100 to, inaccordance with the determination that the vehicle is proximate to aconstruction zone, cause the moving vehicle (e.g., with vehicle movingenabling module 1408) to move in accordance with the construction zoneindicators.

In some embodiments processing unit 110 may cause system 100 todetermine (e.g., with determining module 1404), based on the one or moreindicators, that the construction zone is a complex construction zone,and in accordance with a determination that the construction zone is acomplex construction zone, disable (e.g., with vehicle control disablingmodule) automated vehicle control functionality.

FIG. 15 illustrates a process 1500 for implementing lane constraintprediction consistent with the disclosed embodiments. In someembodiments, the steps in process 1500 may be illustrated by thesituations illustrated in FIG. 13. According to some embodiments,process 1500 may be implemented by one or more components of navigationsystem 100, such as at least one processing unit 110. According to someembodiments, process 1500 may be implemented by a system 100 associatedwith a vehicle 200.

At step 1502, in some embodiments, a system receives multiple images ofa roadway in the vicinity of a moving vehicle associated with thesystem. In some embodiments, the one or more images are acquired usingat least one image capture device, such as image capture devices 122,124, and/or 126. The plurality of images may be received by the systemthrough a data interface such as data interface 128. In the exampleillustrated in FIG. 13, system 100 may acquire a plurality of images ofroadway 1300 in the vicinity of vehicle 200.

At step 1504, in some embodiments, the system recognizes, in themultiple images, one or more indicators of a construction zone. In someembodiments, the system may use image analysis techniques to process theone or more images received to detect signs, objects, other vehicles,marked lane lines, and any other objects or characteristics of asurrounding environment. In some embodiments, certain signs, markings,lane lines, objects, and/or combinations of any such detected aspectsmay be predetermined by the system to be indicators of a constructionzone. In some embodiments, cones, barrels, flashing lights, poles,barriers, and/or markers present in the vicinity of the roadway may bepredetermined by the system to be indicators of a construction zone. Insome embodiments, predefined traffic signs, such as those bearing text,symbols, or images known to indicate a construction zone, may bepredetermined by the system to be indicators of a construction zone. Insome embodiments, lane lines may be determined by the system to beindicators of a construction zone. For example, in Germany, constructionzones may be marked with special construction zone lane lines painted onroadways, the construction zone lane lines possibly being of a differentcolor or a different appearance than conventional lane lines. The systemmay detect lane lines of a color, appearance, orientation, and/orlocation that indicates to the system that the lines are constructionzone lane lines, and the system may recognize such construction zonelane lines as indicators of a construction zone.

In the example of FIG. 13, system 100 may recognize cones 1316,construction road signs 1318 a and 1318 b, and/or painted constructionzone lane line 1326. In some embodiments, any or all of those elementsmay be predetermined by system 100, or dynamically determined by system100, to be construction zone indicators.

At step 1506, in some embodiments, the system determines, based on theone or more indicators, whether the vehicle is proximate to aconstruction zone. In some embodiments, a system may determine based onthe proximity (to the vehicle, roadway, or lane) of construction zoneindicators that the vehicle is proximate to a construction zone. In someembodiments, a system may assume based on the orientation or placementwith respect to a roadway or a lane that the vehicle is proximate to aconstruction zone. In some embodiments, a system may determine from thecontent (e.g., the images, symbols, and/or text) of one or more roadsigns that the vehicle is proximate to a construction zone. In someembodiments, a minimum threshold number of construction zone indicatorsmust be detected within a maximum threshold time of one another in orderfor a system to determine that the vehicle is proximate to aconstruction zone. In the example of FIG. 13, system 100 may determinebased on the simultaneous detection of cones 1316, construction roadsigns 1318 a and 1318 b, and/or painted construction zone lane line 1326that vehicle 200 is proximate to a construction zone.

In some embodiments, if it is determined, at block 1506, that thevehicle is not proximate to a construction zone, then method 1500 mayreturn to step 1502 and may continue to acquire additional images of theroadway as time progresses.

In some embodiments, if it is determined, at block 1506, that thevehicle is proximate to a construction zone, then method 1500 mayproceed to step 1508. At step 1508, in some embodiments, in accordancewith a determination that the vehicle is proximate to a constructionzone, the system outputs a signal indicating that the vehicle isproximate to a construction zone. In some embodiments, the signal outputby system 100 may be an electronic signal or an electronic message thatcommunicates to any component of the system or to any associated systemthat a construction zone is detected. For example, a signal may be sentto a display system associated with the system, or a signal may be sentto any other local or remote system communicatively coupled with thesystem. In some embodiments, the signal may be perceptible by a driverof the vehicle. For example, the signal may be a visible signal (such asa lighted indicator or a displayed element on a computer orentertainment system display), an audible signal emitted by a speakersystem, and/or a haptic signal such as a vibration.

At step 1510, in some embodiments, in accordance with the determinationthat the vehicle is proximate to a construction zone, the system enablesthe moving vehicle to move in accordance with the construction zoneindicators. In some embodiments, in response to determining that thevehicle is proximate to a construction zone, the system may cause thevehicle to be accelerated, decelerated, steered, or otherwise controlledand moved in accordance with the detected construction zone indicators.In some embodiments, the vehicle may be automatically slowed to areduced speed to increase safety. In some embodiments, the vehicle maybe steered or caused to change lanes so as to follow construction zonelane lines, avoid cones/barrels/poles/barriers or other constructionzone indicators, or follow instructions indicated on road signsindicating or associated with the construction zone. In the example ofFIG. 13, system 100 may modify a course of travel of vehicle 200 suchthat vehicle 200 follows detected lane constraint 1314 in accordancewith construction zone lane line 1326 (rather than lane constraint 1312detected in accordance with lane line 1324), avoids cones 1316, andobeys traffic sign 1318 b (which includes a symbol instructing vehicle200 to merge left). In some embodiments, vehicle 200 may be caused toaccordingly merge from lane 1304 left into lane 1302.

At step 1512, in some embodiments, the system determines, based on theone or more indicators, whether the construction zone is a complexconstruction zone. In some embodiments, a complex construction zone maybe a construction zone having one or more predefined or dynamicallydetermined characteristics associated by the system with a constructionzone in which autonomous and/or driver-assist control of a vehicle maybe unsafe, in which autonomous and/or driver assist control of a vehiclemay be unreliable or undependable, or in which control of a vehicleshould be returned to a driver of the vehicle. In some embodiments, acomplex construction zone may be a construction zone where anobstruction exists in a lane or on a roadway on or in which the vehicleis traveling. In some embodiments, a complex construction zone may be aconstruction zone in which a predefined or dynamically determinedthreshold number of cones/barrels/poles/barriers or other constructionzone indicators are detected within a threshold distance or thresholdtime of one another. That is, in some embodiments, when a constructionzone is densely populated with construction zone indicators, it may bedetermined to be a complex construction zone. In the example of FIG. 13,in some embodiments, roadway 1300 may be determined by system 100 tocontain a complex construction zone, based on the presence of cones 1316on roadway 1300, cones 1316 in lane 1304, a minimum threshold number ofcones 1316 being detected, and or a minimum threshold number of totalconstruction zone indicators being detected.

In some embodiments, if it is determined, at block 1512, that theconstruction zone is not a complex construction zone, then method 1500may return to step 1502 and may continue to acquire additional images ofthe roadway as time progresses.

In some embodiments, if it is determined, at block 1512, that theconstruction zone is a complex construction zone, then method 1500 mayproceed to step 1514. At step 1514, in some embodiments, in accordancewith a determination that the construction zone is a complexconstruction zone, the system may disable automated vehicle controlfunctionality. In some embodiments, driver assist functionality may bedisabled. In some embodiments, fully automated driving functionality maybe disabled. In some embodiments, full control (e.g., acceleration,deceleration, steering, etc.) of vehicle 200 may be returned by system100 to the driver of vehicle 200.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets. Further, it is to be understood that the functionality ofthe program modules described herein is not limited to the particulartype of program module named herein, but rather can be programmed in anysuitable manner across among one or more modules or other softwareconstructs.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1. (canceled)
 2. A device mountable on a vehicle, comprising: a cameraadapted to capture multiple images of a roadway within a field of viewof the camera; and a processor configured to: apply image processing tothe multiple images to recognize, in the multiple images, an indicatorof a construction zone, the indicator of the construction zone includingat least one of: cones, barrels, or poles present in the vicinity of theroadway, or painted lines or barriers present on the roadway; and outputa signal in response to recognizing the one or more indicators of theconstruction zone, the signal used to initiate a navigational responseof the vehicle.
 3. The device of claim 2, wherein the navigationalresponse includes causing the vehicle to discontinue autonomous drivingmode.
 4. The device of claim 2, wherein the navigational responseincludes causing the vehicle to activate a construction zone drivingmode.
 5. The device of claim 2, wherein initiating the navigationalresponse of the vehicle comprises one of: causing the vehicle to reducespeed; causing the vehicle to change a lane in which the vehicle istraveling; enabling avoiding cones, barrels, poles, or barriers; orcausing the vehicle to follow construction zone lane constraints ratherthan conventional lane constraints.
 6. The device of claim 2, whereinthe processor is configured to: determine, based on the indicator of theconstruction zone, that the construction zone is a complex constructionzone; and disable automated vehicle control functionality based on thedetermination.
 7. The device of claim 6, wherein the determination thatthe construction zone is a complex construction zone comprises therecognition of one of: cones present in a lane in which the vehicle ispresent, barrels present in a lane in which the vehicle is present,poles present in a lane in which the vehicle is present, or barrierspresent in a lane in which the vehicle is present.
 8. The device ofclaim 6, wherein the determination that the construction zone is acomplex construction zone comprises the recognition that a number ofcones, barrels, poles, or barriers in the vicinity of the vehicle, aloneor in combination, exceed a predefined threshold number.
 9. The deviceof claim 6, wherein the processor is configured to: monitor the roadwayto continually determine whether the construction zone is a complexconstruction zone using additional images obtained from the camera. 10.A device mountable on a vehicle, comprising: a camera adapted to capturemultiple images of a roadway within a field of view of the camera; and aprocessor configured to: apply image processing to the multiple imagesto recognize, in the multiple images, an indicator of a constructionzone, the indicator of the construction zone including an apparatus tocontrol traffic flow over the roadway; and output a signal in responseto recognizing the one or more indicators of the construction zone, thesignal used to alter an autonomous navigational mode of the vehicle. 11.The device of claim 10, wherein the apparatus includes at least one of:cones, barrels, or poles present in the vicinity of the roadway, orbarriers present on the roadway.
 12. The device of claim 10, wherein toalter the autonomous navigational mode of the vehicle, the signal causesthe vehicle to discontinue autonomous driving mode.
 13. The device ofclaim 10, wherein to alter the autonomous navigational mode of thevehicle, the signal causes the vehicle to activate a construction zonedriving mode.
 14. The device of claim 10, wherein the processor isconfigured to initiate a navigational response of the vehicle based onthe indicator of the construction zone.
 15. The device of claim 14,wherein the navigational response of the vehicle comprises one of:causing the vehicle to reduce speed; causing the vehicle to change alane in which the vehicle is traveling; enabling avoiding cones,barrels, poles, or barriers; or causing the vehicle to followconstruction zone lane constraints rather than conventional laneconstraints.
 16. A computer system comprising: a memory that storesinstructions; and a processor that executes the instructions to causethe system to: apply image processing to the multiple images torecognize, in the multiple images, an indicator of a construction zone,the indicator of the construction zone including an apparatus to controltraffic flow over the roadway; and output a signal in response torecognizing the one or more indicators of the construction zone, thesignal used to alter an autonomous navigational mode of the vehicle. 17.The computer system of claim 16, wherein the apparatus includes at leastone of: cones, barrels, or poles present in the vicinity of the roadway,or barriers present on the roadway.
 18. The computer system of claim 16,wherein to alter the autonomous navigational mode of the vehicle, thesignal causes the vehicle to discontinue autonomous driving mode.
 19. Avehicle, comprising: a body; a camera adapted to capture, when mountedon the vehicle, and while the vehicle is in motion, multiple images of aroadway within a field of view of the camera; and a processor configuredto: apply image processing to the multiple images to recognize, in themultiple images, an indicator of a construction zone, the indicator ofthe construction zone including an apparatus to control traffic flowover the roadway; and output a signal in response to recognizing the oneor more indicators of the construction zone, the signal used to alter anautonomous navigational mode of the vehicle.
 20. The vehicle of claim19, wherein the image processing is performed using monocular imageanalysis.